Low-voltage transformer area AC / DC power distribution network configuration method considering long-term uncertainty of source load

Abstract

The invention relates to a low-voltage transformer area AC / DC power distribution network configuration method considering long-term uncertainty of a source load. The method comprises the following steps: establishing a low-voltage transformer area AC / DC power distribution network configuration model based on two-stage robust optimization by considering the long-term uncertainty of the source load; the low-voltage transformer area AC / DC power distribution network configuration model comprises an energy storage and soft switching planning configuration model taking the lowest operation cost as a target and a low-voltage AC / DC power distribution network long-term scheduling model combined with a long-term uncertainty set; and converting the two-stage problem of the low-voltage transformer area AC / DC power distribution network configuration model into a main problem and a sub-problem in a mixed integer linear form, and solving the main problem and the sub-problem to obtain a low-voltage transformer area AC / DC power distribution network configuration scheme. According to the invention, a steady planning scheme can be provided for large-scale access of renewable energy sources and rapid increase of direct-current loads in the future.

Description

Technical Field

[0001] This invention relates to the field of distribution network planning and optimization configuration technology, and in particular to a low-voltage AC / DC distribution network configuration method that considers the long-term uncertainty of source load. Background Technology

[0002] With the increasing prevalence of distributed photovoltaic (PV), wind power, energy storage (ESS) devices, and electric vehicles, both the power supply and load sides of low-voltage distribution networks exhibit significant time-varying and uncertainties. Traditional static distribution network planning methods struggle to adapt to multiple factors, including fluctuations in renewable energy generation, increased user load, and surging charging demand, posing significant challenges to system safety and economic efficiency. Simultaneously, DC loads (such as data centers, DC lighting, and DC charging stations) are rapidly developing under the new energy internet architecture, making AC-based configuration schemes increasingly limited in terms of power supply reliability and energy efficiency.

[0003] To simultaneously meet the efficient power supply needs of both AC and DC loads, hybrid AC / DC distribution networks are gaining increasing attention. This architecture, by arranging AC feeders and DC buses in parallel on the low-voltage side and integrating power electronic converters, enables targeted power supply to different types of loads, significantly reducing multiple converter losses and improving feeder operational flexibility. Furthermore, hybrid networks can better integrate energy storage units to buffer short-term fluctuations in renewable energy generation, while coordinating peak charging and discharging times to optimize overall network operating costs.

[0004] At the district-level planning level, the core issue in configuring hybrid AC / DC distribution networks lies in how to rationally determine the routing of AC feeders, the location of DC buses, and power electronic conversion stations, and based on this, allocate energy storage capacity and operating strategies. Considering the long-term uncertainties in the installed capacity of renewable energy and user-side loads over the next few years, the configuration model must take into account generation-load matching. Relying solely on short-term configuration schemes often leads to significant risks of insufficient safety margins or resource waste in actual operation. Therefore, long-term planning is necessary to minimize overall investment and operating costs while ensuring system safety. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a low-voltage AC / DC distribution network configuration method that takes into account the long-term uncertainty of source load, which can provide a robust planning scheme for the large-scale access of renewable energy and the rapid growth of DC load in the future.

[0006] The technical solution adopted by this invention to solve its technical problem is: to provide a low-voltage AC / DC distribution network configuration method considering the long-term uncertainty of source load, comprising the following steps:

[0007] Considering the long-term uncertainty of source and load, a low-voltage AC / DC distribution network configuration model based on two-stage robust optimization is established; the low-voltage AC / DC distribution network configuration model includes an energy storage and soft-switching planning and configuration model with the goal of minimizing operating costs and a long-term dispatch model of the low-voltage AC / DC distribution network that incorporates long-term uncertainty sets;

[0008] The two-stage problem of the low-voltage distribution area AC / DC distribution network configuration model is transformed into a main problem and sub-problems with mixed integer linear form, and solved to obtain the low-voltage distribution area AC / DC distribution network configuration scheme.

[0009] The objective function of the energy storage and soft-switching planning and configuration model is: Among them, f mid For the operating costs of energy storage devices and soft switching devices, N ESS This refers to the number of energy storage devices connected to the power distribution network. Let N be the active power loss value of the i-th energy storage device in stage t. SOP This refers to the number of soft switching devices connected in the distribution network. Let t be the active power loss value of the i-th soft-switching device in stage t, and T be the entire optimization period.

[0010] The constraints of the energy storage and soft-switching planning and configuration model are as follows:

[0011]

[0012] Where, N SOP N represents the number of soft switching devices connected to the distribution network. ESS N represents the number of energy storage devices connected to the distribution network. SOP,max N represents the upper limit for the number of soft switching devices connected in the distribution network. ESS,max S represents the upper limit for the number of energy storage devices connected to the distribution network. S,i S represents the capacity of the i-th soft-switching device. SOP,max P represents the maximum capacity that a single soft-switching device can be installed with. ESS,i Let P be the capacity of the i-th energy storage device. ESS,max P represents the maximum capacity that a single energy storage device can be installed at. ij,t and Q ij,t Let r represent the active power and reactive power flowing through branch ij at time t, respectively. ij and x ij F represents the resistance and reactance of branch ij. ij,t Let be the square of the current flowing through branch ij at time t. This represents the active power of the energy storage device at node i at time t. and The active and reactive power of the photovoltaic system at node j at time t, respectively. t con and V represents the active load and reactive load at time t, respectively. i,t and V j,t Let represent the squares of the voltages at nodes i and j at time t, respectively. and V represents the active power and reactive power transmitted by the soft-switching device at node i at time t, respectively. min and V max Let F represent the squares of the lower and upper voltage limits of node i at time t, respectively. min and F max Let represent the squares of the lower and upper limits of the current flowing through branch ij at time t, respectively. and Let δ be the charging power and discharging power of the energy storage device at node i at time t. C,t δ is the charging indicator variable for energy storage devices. D,t For the discharge indication variable of energy storage devices, This represents the maximum power of the energy storage device.

[0013] The objective function of the long-term dispatch model for the low-voltage AC / DC distribution network is: Where, r ij Let F be the resistance of branch ij. ij,t V is the square of the current flowing through branch ij at time t. r For voltage deviation, P t PV P represents the photovoltaic output at time t. PV For photovoltaic configuration capacity, P t con To represent the active load at time t, P con Configure capacity for active power.

[0014] The constraints of the long-term dispatch model for the low-voltage AC / DC distribution network are:

[0015]

[0016] Among them, P ij,t and Q ij,t Let r represent the active power and reactive power flowing through branch ij at time t, respectively. ij and x ij F represents the resistance and reactance of branch ij. ij,t Let be the square of the current flowing through branch ij at time t. This represents the active power of the energy storage device at node i at time t. and The active and reactive power of the photovoltaic system at node j at time t, respectively. t con and V represents the active load and reactive load at time t, respectively. i,t and V j,t Let represent the squares of the voltages at nodes i and j at time t, respectively. and S represents the active power and reactive power transmitted by the soft-switching device at node i at time t, respectively. S,i V represents the capacity of the i-th soft-switching device. min and V max Let F represent the squares of the lower and upper voltage limits of node i at time t, respectively. min and F max Let represent the squares of the lower and upper limits of the current flowing through branch ij at time t, respectively. and Let δ be the charging power and discharging power of the energy storage device at node i at time t. C,t δ is the charging indicator variable for energy storage devices. D,t For the discharge indication variable of energy storage devices, V represents the maximum power of the energy storage device. r V represents the voltage deviation, V0 represents the node reference voltage, and V i This represents the node voltage.

[0017] The long-term uncertain set is: Among them, P t PV P represents the photovoltaic output at time t. t con P represents the active power load at time t. PV For photovoltaic configuration capacity, P con Configure capacity for active power. For photovoltaic reference values, θ pv,t Let be the percentage of photovoltaic penetration at time t. θ is the load reference value. con,t Let A be the percentage increase in load at time t, and let A() represent the set of uncertain variables.

[0018] The main problem is expressed in a compact form as follows: Where x represents the first-stage configuration decision; y represents the second-stage decision; a represents the worst-case cost upper bound auxiliary variable; c represents the coefficient vector; D and K are the corresponding matrices of the second-stage constraints, F and G are the configuration and emergency response constraint matrices; d, e, and h are the right-hand vectors of the second-stage constraints; u represents the uncertainty set, I u The selection matrix has fixed uncertain parameters; the subproblem is represented in compact form as follows: Where γ, λ, v, π are dual variables.

[0019] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the steps of the above-mentioned low-voltage distribution area AC / DC distribution network configuration method considering the long-term uncertainty of source load.

[0020] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the above-mentioned low-voltage distribution area AC / DC distribution network configuration method considering the long-term uncertainty of source load are implemented.

[0021] Beneficial effects

[0022] By adopting the above-mentioned technical solutions, this invention has the following advantages and positive effects compared with existing technologies: This invention considers the temporal fluctuations and growth uncertainties of renewable energy and load sides during future operation, generates multi-stage scenarios, and selects equipment locations and configures capacity based on the worst-case scenario within a robust optimization framework. This allows for a precise characterization of long-term uncertainty features, effectively improving the robustness and economy of the planning scheme under worst-case conditions. This invention simultaneously considers AC feeder routing, DC bus layout, SOP (Start of Production) and the installation location and capacity of energy storage devices within the same optimization model. Combined with the polyhedral relaxation technique of second-order cone constraints, it maximizes the complementary advantages between AC and DC networks, reduces multi-stage converter losses, and improves overall network energy efficiency. Attached Figure Description

[0023] Figure 1 This is a flowchart of the low-voltage distribution area AC / DC distribution network configuration method according to the first embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of a power distribution network configuration with low-voltage flexible interconnection equipment after adopting the first embodiment of the present invention. Detailed Implementation

[0025] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0026] The first embodiment of this invention relates to a low-voltage distribution area AC / DC distribution network configuration method considering long-term uncertainties in source loads. This method establishes a low-voltage distribution area AC / DC distribution network configuration model based on two-stage robust optimization, taking into account long-term uncertainties in source loads. Through model derivation and transformation, the two-stage problem is transformed into a main problem and sub-problems with mixed-integer linear form, and solved to obtain the low-voltage distribution area AC / DC distribution network configuration scheme. An uncertainty adjustment parameter is added to this low-voltage distribution area AC / DC distribution network configuration model, allowing AC / DC distribution network dispatchers to flexibly select the conservatism level of the dispatch scheme. Figure 1 As shown, the specific steps include:

[0027] Step 1: Considering the long-term uncertainty of source load, establish a low-voltage distribution area AC / DC distribution network configuration model based on two-stage robust optimization.

[0028] The low-voltage AC / DC distribution network configuration model established in this step includes the first-stage energy storage and soft-switching planning and configuration model aimed at minimizing operating costs, and the second-stage long-term dispatch model of the low-voltage AC / DC distribution network that combines long-term uncertain sets.

[0029] The first phase of the energy storage and soft switching planning and configuration model selects the installation location Z of the soft switching (SOP) equipment connected to the low-voltage distribution network. S,i With installation capacity S S,i And the selection of the installation location Z for energy storage devices connected to the low-voltage distribution network. E,i With installation capacity S E,i As the optimization decision variable, and with the goal of minimizing the operating costs of energy storage devices and SOP devices, the objective function can be expressed as:

[0030]

[0031] Among them, f mid For the operating costs of energy storage devices and soft switching devices, N ESS This refers to the number of energy storage devices connected to the power distribution network. Let N be the active power loss value of the i-th energy storage device in stage t. SOP This refers to the number of soft switching devices connected in the distribution network. Let t be the active power loss value of the i-th soft-switching device in stage t, and T be the entire optimization period.

[0032] The constraints of the energy storage and soft-switching planning and configuration model are as follows:

[0033]

[0034] Where, N SOP,max N represents the upper limit for the number of soft switching devices connected in the distribution network. ESS,maxS represents the upper limit for the number of energy storage devices connected to the distribution network. S,i S represents the capacity of the i-th soft-switching device. SOP,max P represents the maximum capacity that a single soft-switching device can be installed with. ESS,i Let P be the capacity of the i-th energy storage device. ESS,max P represents the maximum capacity that a single energy storage device can be installed at. ij,t and Q ij,t Let r represent the active power and reactive power flowing through branch ij at time t, respectively. ij and x ij F represents the resistance and reactance of branch ij. ij,t Let be the square of the current flowing through branch ij at time t. This represents the active power of the energy storage device at node i at time t. and The active and reactive power of the photovoltaic system at node j at time t, respectively. t con and V represents the active load and reactive load at time t, respectively. i,t and V j,t Let represent the squares of the voltages at nodes i and j at time t, respectively. and V represents the active power and reactive power transmitted by the soft-switching device at node i at time t, respectively. min and V max Let F represent the squares of the lower and upper voltage limits of node i at time t, respectively. min and F max Let represent the squares of the lower and upper limits of the current flowing through branch ij at time t, respectively. and Let δ be the charging power and discharging power of the energy storage device at node i at time t. C,t δ is the charging indicator variable for energy storage devices. D,t For the discharge indication variable of energy storage devices, This represents the maximum power of the energy storage device.

[0035] The objective function of the long-term dispatch model for the low-voltage AC / DC distribution network in the second stage can be expressed as:

[0036]

[0037] Among them, V r For voltage deviation, P t PV P represents the photovoltaic output at time t. PV For photovoltaic configuration capacity, P t con To represent the active load at time t, Pcon Configure capacity for active power.

[0038] The constraints of this long-term dispatch model for the low-voltage AC / DC distribution network are:

[0039]

[0040] Among them, V r V represents the voltage deviation, V0 represents the node reference voltage, and V i This represents the node voltage.

[0041] The long-term uncertain set is:

[0042] (P t PV ,P t con )∈A(P pv ,P con )

[0043]

[0044] Among them, P t PV P represents the photovoltaic output at time t. t con P represents the active power load at time t. PV For photovoltaic configuration capacity, P con Configure capacity for active power. For photovoltaic reference values, θ pv,t Let be the percentage of photovoltaic penetration at time t. θ is the load reference value. con,t Let A be the percentage increase in load at time t, and let A() represent the set of uncertain variables.

[0045] Step 2: Transform the two-stage problem of the low-voltage distribution area AC / DC distribution network configuration model into a main problem and sub-problems with mixed integer linear form, and solve them to obtain the low-voltage distribution area AC / DC distribution network configuration scheme.

[0046] When solving the AC / DC distribution network configuration model of the low-voltage distribution area mentioned above, its two stages can be decomposed into master-subproblems through duality.

[0047] The main problem is represented in compact form as follows:

[0048]

[0049] Where x represents the first-stage configuration decision; y represents the second-stage decision; a represents the worst-case cost upper bound auxiliary variable; c represents the coefficient vector; D and K represent the corresponding matrices of the second-stage constraints (such as power flow and power balance); F and G represent the configuration and emergency response constraint matrices; d, e, and h represent the right-hand vectors of the second-stage constraints; u represents the uncertainty set; and I represents the set of uncertainties. u A selection matrix with fixed uncertain parameters (faults, renewable output, etc.).

[0050] The subproblem is represented in compact form as follows:

[0051]

[0052] Where γ, λ, v, π are dual variables.

[0053] For the AC / DC distribution network configuration model of this low-voltage distribution area, column and constraint generation (C&CG) is used to dualize the subproblem min problem into a max problem. The single-layer max problem is then substituted into a commercial solver to obtain the AC / DC distribution network configuration scheme for the low-voltage distribution area (see...). Figure 2 ).

[0054] It is easy to see that this invention considers the temporal fluctuations and growth uncertainties of renewable energy and loads during future operation, generates multi-stage scenarios, and selects equipment locations and configures capacity based on the worst-case scenario within a robust optimization framework. This can accurately characterize long-term uncertainty features and effectively improve the robustness and economy of the planning scheme under the worst-case scenario. This invention simultaneously considers AC feeder routing, DC bus layout, SOPs, and the installation location and capacity of energy storage devices within the same optimization model. Combined with the polyhedral relaxation technique of second-order cone constraints, it maximizes the complementary advantages between AC and DC networks, reduces multi-stage converter losses, and improves the overall network energy efficiency.

[0055] The second embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-voltage distribution area AC / DC distribution network configuration method considering long-term uncertainty of source load in the first embodiment.

[0056] The third embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the low-voltage distribution area AC / DC distribution network configuration method considering long-term uncertainty of source load in the first embodiment.

[0057] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0058] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for configuring a low-voltage distribution network (AC / DC) in a distribution area considering long-term uncertainties in source loads, characterized in that, Includes the following steps: Considering the long-term uncertainty of source and load, a low-voltage AC / DC distribution network configuration model based on two-stage robust optimization is established; the low-voltage AC / DC distribution network configuration model includes an energy storage and soft-switching planning and configuration model with the goal of minimizing operating costs and a long-term dispatch model of the low-voltage AC / DC distribution network that incorporates long-term uncertainty sets; The two-stage problem of the low-voltage distribution area AC / DC distribution network configuration model is transformed into a main problem and sub-problems with mixed integer linear form, and solved to obtain the low-voltage distribution area AC / DC distribution network configuration scheme.

2. The method for configuring a low-voltage distribution network (AC / DC) considering long-term uncertainty of source and load as described in claim 1, characterized in that, The objective function of the energy storage and soft-switching planning and configuration model is: Among them, f mid For the operating costs of energy storage devices and soft switching devices, N ESS This refers to the number of energy storage devices connected to the power distribution network. Let N be the active power loss value of the i-th energy storage device in stage t. SOP This refers to the number of soft switching devices connected in the distribution network. Let t be the active power loss value of the i-th soft-switching device in stage t, and T be the entire optimization period.

3. The method for configuring a low-voltage distribution network (AC / DC) considering long-term uncertainty of source and load as described in claim 1, characterized in that, The constraints of the energy storage and soft-switching planning and configuration model are as follows: Where, N SOP N represents the number of soft switching devices connected to the distribution network. ESS N represents the number of energy storage devices connected to the distribution network. SOP,max N represents the upper limit for the number of soft switching devices connected in the distribution network. ESS,max S represents the upper limit for the number of energy storage devices connected to the distribution network. S,i S represents the capacity of the i-th soft-switching device. SOP,max P represents the maximum capacity that a single soft-switching device can be installed with. ESS,i Let P be the capacity of the i-th energy storage device. ESS,max P represents the maximum capacity that a single energy storage device can be installed at. ij,t and Q ij,t Let r represent the active power and reactive power flowing through branch ij at time t, respectively. ij and x ij F represents the resistance and reactance of branch ij. ij,t Let be the square of the current flowing through branch ij at time t. This represents the active power of the energy storage device at node i at time t. and The active and reactive power of the photovoltaic system at node j at time t, respectively. t con and V represents the active load and reactive load at time t, respectively. i,t and V j,t Let represent the squares of the voltages at nodes i and j at time t, respectively. and V represents the active power and reactive power transmitted by the soft-switching device at node i at time t, respectively. min and V max Let F represent the squares of the lower and upper voltage limits of node i at time t, respectively. min and F max Let represent the squares of the lower and upper limits of the current flowing through branch ij at time t, respectively. and Let δ be the charging power and discharging power of the energy storage device at node i at time t. C,t δ is the charging indicator variable for energy storage devices. D,t For the discharge indication variable of energy storage devices, This represents the maximum power of the energy storage device.

4. The method for configuring a low-voltage distribution network (AC / DC) considering long-term uncertainty of source and load as described in claim 1, characterized in that, The objective function of the long-term dispatch model for the low-voltage AC / DC distribution network is: Where, r ij Let F be the resistance of branch ij. ij,t V is the square of the current flowing through branch ij at time t. r For voltage deviation, P t PV P represents the photovoltaic output at time t. PV For photovoltaic configuration capacity, P t con To represent the active load at time t, P con Configure capacity for active power.

5. The method for configuring a low-voltage distribution network (AC / DC) considering long-term uncertainty of source and load as described in claim 1, characterized in that, The constraints of the long-term dispatch model for the low-voltage AC / DC distribution network are: Among them, P ij,t and Q ij,t Let r represent the active power and reactive power flowing through branch ij at time t, respectively. ij and x ij F represents the resistance and reactance of branch ij. ij,t Let be the square of the current flowing through branch ij at time t. This represents the active power of the energy storage device at node i at time t. and The active and reactive power of the photovoltaic system at node j at time t, respectively. t con and V represents the active load and reactive load at time t, respectively. i,t and V j,t Let represent the squares of the voltages at nodes i and j at time t, respectively. and S represents the active power and reactive power transmitted by the soft-switching device at node i at time t, respectively. S,i V represents the capacity of the i-th soft-switching device. min and V max Let F represent the squares of the lower and upper voltage limits of node i at time t, respectively. min and F max Let represent the squares of the lower and upper limits of the current flowing through branch ij at time t, respectively. and Let δ be the charging power and discharging power of the energy storage device at node i at time t. C,t δ is the charging indicator variable for energy storage devices. D,t For the discharge indication variable of energy storage devices, V represents the maximum power of the energy storage device. r V represents the voltage deviation, V0 represents the node reference voltage, and V i This represents the node voltage.

6. The method for configuring a low-voltage distribution network (AC / DC) considering long-term uncertainty of source and load as described in claim 1, characterized in that, The long-term uncertain set is: Among them, P t PV P represents the photovoltaic output at time t. t con P represents the active power load at time t. PV For photovoltaic configuration capacity, P con Configure capacity for active power. For photovoltaic reference values, θ pv,t Let be the percentage of photovoltaic penetration at time t. θ is the load reference value. con,t Let A be the percentage increase in load at time t, and let A() represent the set of uncertain variables.

7. The method for configuring a low-voltage distribution network (AC / DC) considering long-term uncertainty of source and load as described in claim 1, characterized in that, The main problem is expressed in a compact form as follows: Where x represents the first-stage configuration decision; y represents the second-stage decision; a represents the worst-case cost upper bound auxiliary variable; c represents the coefficient vector; D and K are the corresponding matrices of the second-stage constraints, F and G are the configuration and emergency response constraint matrices; d, e, and h are the right-hand vectors of the second-stage constraints; u represents the uncertainty set, I u The selection matrix has fixed uncertain parameters; the subproblem is represented in compact form as follows: Where γ, λ, v, π are dual variables.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the low-voltage AC / DC distribution network configuration method for considering long-term uncertainty of source load as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-voltage AC / DC distribution network configuration method for considering long-term uncertainty of source load as described in any one of claims 1-7.

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